Engineering Manager, Core Cloud Platform

Lambda•San Francisco, CA

2d•$297,000 - $440,000•Hybrid

About The Position

Lambda’s Core Cloud Platform organization builds the software and services that turn large-scale GPU infrastructure into reliable, customer-facing cloud products. The organization owns foundational platform capabilities across Managed Kubernetes, resource lifecycle, workload orchestration, cloud APIs, automation, observability, and multi-region operations. The role’s immediate focus includes evolving the Managed Kubernetes platform to make provisioning, upgrading, operating, and scaling clusters for demanding AI workloads easier and safer. The platform integrates with Lambda’s compute, networking, storage, fleet, and security systems. The Engineering Manager leads a team building and operating foundational platform services and owns people management, execution, technical quality, and production outcomes while partnering with senior engineers on technical direction. The role requires presence in Lambda’s San Francisco, San Jose, or Bellevue office four days per week; the designated work-from-home day is currently Tuesday.

Requirements

At least 4 years of engineering management experience, with a demonstrated track record of growing high-performing teams.
At least 7 years of software engineering individual-contributor experience, including principal- or staff-level ownership of mission-critical distributed systems.
At least 8 years implementing business-critical services in Python or Go, from conception through production.
Experience building systems that coordinate across independently owned infrastructure services such as compute, networking, and storage without directly owning those services.
Background in multi-region deployment strategies, cross-datacenter state management, and high-availability architectures.
Strong track record of owning projects end to end, including architecture, delivery, operations, and continuous improvement.
Excellent cross-functional collaboration and demonstrated ability to drive alignment across infrastructure, platform, and product teams.
Ability to be present in the San Francisco, San Jose, or Bellevue office four days per week.

Nice To Haves

Awareness of the evolving bare-metal and GPU infrastructure ecosystem, including tools such as NVIDIA NiCo (NCX Infra Controller), DPU-enforced lifecycle automation, BlueField DPU capabilities, and emerging standards for multi-tenant bare-metal management.
Experience evaluating or adopting vendor infrastructure tooling into an existing platform, including full migrations and assessment of fit, risk, and timing.
Familiarity with Kubernetes or Slurm; useful context but not required.
Experience in the AI, ML, or GPU hardware industry.
Proven track record designing large-scale cloud IaaS platforms from the ground up.
Exposure to IPO readiness, enterprise compliance, or security requirements at scale.

Responsibilities

Lead the design, implementation, and production operation of foundational cloud-platform services.
Own the technical roadmap for making the platform more distributed, resilient, observable, and operationally efficient.
Ensure reliable platform operation across a growing multi-datacenter and multi-region fleet, including handling partial failures and long-running workflows.
Partner with senior and staff engineers on architectural decisions across distributed services, stateful systems, APIs, schedulers, controllers, and automation.
Define contracts and integration patterns between the cloud platform and compute, networking, fleet, security, and customer-facing systems.
Lead and grow a team of senior and staff engineers; hire, develop people, maintain a high-performance bar, and address underperformance.
Own project planning, prioritization, and delivery aligned with business objectives and customer commitments.
Establish engineering practices across design reviews, testing, CI/CD, observability, incident response, and on-call operations.
Collaborate with peer Engineering Managers and cross-functional partners to resolve dependencies and deliver customer outcomes.
Improve engineering leverage through automation, effective operating mechanisms, and responsible use of AI-assisted engineering.
Stay current with Kubernetes, cloud-native infrastructure, GPU orchestration, and platform automation, applying developments where they materially improve the platform.